Fitting two human atrial cell models to experimental data using Bayesian history matching

Fitting hyperelastic models to experimental data

This paper is concerned with determining material parameters in incompressible isotropic elastic strain–energy functions on the basis of a non-linear least squares optimization method by fitting data from the classical experiments of Treloar and Jones and Treloar on natural rubber. We consider three separate forms of strain-energy function, based respectively on use of the principal stretches, ...

Matching dendritic neuron models to experimental data.

Mathematical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases; Laboratory of Neural Control, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland; Department of Biological Sciences, Ohio University, Athens, Ohio; University Laboratory of Physiology, Oxford, United Kingdom; Division of Neuroscience, John Curtin School of Medical Research, A...

Alternative Methods for Fitting Two-Stage Hierarchical Bayesian Models

Although it is common practice to fit a complex Bayesian model using Markov chain Monte Carlo (MCMC) methods, we provide an alternative sampling-based method to fit a two-stage hierarchical model in which there is conjugacy conditional on the parameters in the second stage. Using the sampling/importance resampling (SIR) algorithm, our method subsamples independent samples from an approximate jo...

Fitting Models to Grouped Data

Bayesian Two-Sample Prediction with Progressively Type-II Censored Data for Some Lifetime Models

Prediction on the basis of censored data is very important topic in many fields including medical and engineering sciences. In this paper, based on progressive Type-II right censoring scheme, we will discuss Bayesian two-sample prediction. A general form for lifetime model including some well known and useful models such asWeibull and Pareto is considered for obtaining prediction bounds ...